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Activity Number: 461 - Bugs, Bugs Everywhere - the Statistics Behind Our Microbiome
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Graphics
Abstract #330899 Presentation
Title: Concomitant Regression Models for Microbiome Data
Author(s): Christian Mueller* and Aditya Mishra and Patrick Combettes
Companies: Flatiron Institute and Flatiron Institute and North Carolina State University
Keywords: compositional data; concomitant estimation; regression; proximal algorithms; microbiome data; robust estimation

Targeted amplicon sequencing data, including 16S rRNA and ITS sequence data, are inherently compositional in nature. Using these data for regression tasks is thus challenging due to the constant sum constraint. In addition, typical microbiome data are overdispersed and zero-inflated. To alleviate the challenges associated with these data, we present novel concomitant regression models for microbiome data where both the regression vector and scales are estimated concomitantly. The presented model estimation tasks admit convex optimization formulations that can be solved efficiently using proximal algorithms. We show improved prediction performance compared to state-of-the-art methods both on synthetic and real microbiome data, ranging from host-associated to environmental amplicon data.

Authors who are presenting talks have a * after their name.

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